Table REF  shows the recognition performance (mAP) of RNet and baseline methods. With the benefit from re-weighting the video imprint, the RNet achieves better results on both CCV (mAP = \(79.9/79.0\) ) and MED14 (mAP = \(36.9/36.3\) ) datasets compared with sum-aggregation and CGA/EPA. In addition, on the CCV dataset, we also employ the same strategy as [1]} to combine motion and audio features with our appearance-based representation. As shown in Table REF , the fusion strategy further boosts the recognition performance (mAP = \(87.1/86.8\) ). On MED14 dataset, the proposed method sets a new performance record (mAP = \(40.2\) ) after fusing motion features (IDT) in a similar way to [2]}, with the additional convenience of simultaneously providing recounting results.
